Throughout the hourlong discussion, panelists repeatedly drew parallels to how -- thanks to digitization -- the collection and examination of patient-level data had changed how doctors and hospitals operated, enabling them to shift increasingly from analyzing what had gone wrong with patients to better diagnosing and treating them.

The flood of data flowing into hospitals "enabled them to start answering different questions," said Mark Milliron, chief learning officer at Civitas Learning and one of the panelists.

The new data revealed among other things that medical errors were the third biggest killer of patients, which, "after some Kubler-Rossian denial," Milliron said, drove the realization that hospitals could improve patient outcomes by "just getting people to wash their hands, run lines the right way, not cut off the wrong limbs."

Gaining access to more patient data earlier in their treatment arc also gave health care workers the ability to intervene earlier with treatments and to "give them the chance to intervene" in changing behavior on factors like blood pressure, Milliron said.

Data are beginning to flow into higher education the same way, moving from "a trickle to a stream to a river," said Milliron, whose company is one of many that, by injecting digital technologies into the learning process, are generating reams of data about their students' interactions with courseware and technology systems. (For instance, Sinclair Community College, an Ohio institution that adopted a data-focused approach on the early side, more than a decade ago, has a terabyte of information about its students, said Laura Mercer, chief of staff to Sinclair's president.)

While critics sometimes bemoan the fact that higher education has come late to the party on data analytics, that may not be a bad thing, Milliron argued. "It's sometimes good to be the second mouse to the cheese, because you can see how others have used it."

Many colleges continue to struggle to marshal the data they have in their various learning management, student information and other technology systems, some of which they may not even own and control. But those that are able to "amalgamate" their sources of data, Mercer said, can stitch together portraits of students that can help institutions like Sinclair "understand that a student is in trouble before they drop a class, or fail out of school," she said. "We can see as early as the second week" that a student is struggling or disengaged, "which gives us time to intercede to possible save that student."

That opening prompted Milliron to return to the medical theme. "Higher education for the last 30 years has been focused on autopsy data," he said, looking after the fact at national longitudinal data about "students who are not with us anymore."

"Now we're getting data about students during the operation, which gives us a chance to ask, 'can we save this student?' " he said. "That is a completely different orientation, from autopsy to operations to diagnostics."

The shift does not come without questions and challenges. The panelists discussed the sorts of questions that Matt Reed raised two weeks again in a post[2] on his Confessions of a Community College Dean blog and were discussed in a followup article in Inside Digital Learning,[3] about whether sharing predictive data with students might unintentionally impede or harm them by playing into stereotypes.

"There is a moral imperative to know" if a student is at risk of failing academically, Milliron said, just as if a doctor might "see that this person has cancer and doesn't tell them." Institutions have historically used data about students to help themselves, to inform legislators making policy decisions, and the like.